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      The High Expression of PTPRH Is Associated with Poor Prognosis of Human Lung Adenocarcinoma

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          Abstract

          Objective

          The aim of the study is to explore the prognosis value of PTPRH in patients with lung adenocarcinoma (LUAD).

          Methods

          Oncomine, UALCAN, and GEPIA databases were employed to examine the differential expression of PTPRH between LUAD and adjacent tissues. 100 pairs of LUAD and adjacent tissue samples were involved in this study. qRT-PCR and immunohistochemical staining were performed. Meanwhile, we analyzed The Cancer Genome Atlas (TCGA) data to investigate the correlation between PTPRH gene expression and clinicopathological characteristics. Kaplan-Meier analysis and univariate and multivariate Cox analyses were performed to estimate the relationship between PTPRH expression and LUAD prognosis. The evaluation performance was verified by drawing a ROC curve. In addition, through GSEA, the changes of PTPRH expression were analyzed by GSEA to screen out primarily affected signaling pathway.

          Results

          Oncomine, UALCAN, and GEPIA databases showed that the mRNA expression of PTPRH in LUAD tissues was significantly higher than that in adjacent tissues. qRT-PCR and immunohistochemical staining indicated the mRNA and protein levels of PTPRH in LUAD tissues were markedly upregulated. TCGA data showed that the expression of PTPRH was significantly correlated with T stage and disease stage. Kaplan-Meier analysis showed that the patients with high PTPRH expression had a poor prognosis. Univariate and multivariate Cox analyses exhibited that PTPRH expression could act as an independent prognostic factor for LUAD. The ROC curve showed that PTPRH combined with various clinicopathological features could effectively predict the prognosis of LUAD. Finally, GSEA indicated that changes in PTPRH expression level may affect p53, VEGF, Notch, and mTOR cancer-related signaling pathways.

          Conclusion

          Our results demonstrated that PTPRH was highly expressed in LUAD and may be closely correlated with the poor prognosis of LUAD patients.

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          GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses

          Zefang Tang, Chenwei Li, Boxi Kang … (2017)
          Abstract Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.
          Article 0 comments Cited 3612 times – based on 0 reviews     Review now
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            UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses1

            Darshan S. Chandrashekar, Bhuwan Bashel, Sai Balasubramanya … (2017)
            Genomics data from The Cancer Genome Atlas (TCGA) project has led to the comprehensive molecular characterization of multiple cancer types. The large sample numbers in TCGA offer an excellent opportunity to address questions associated with tumo heterogeneity. Exploration of the data by cancer researchers and clinicians is imperative to unearth novel therapeutic/diagnostic biomarkers. Various computational tools have been developed to aid researchers in carrying out specific TCGA data analyses; however there is need for resources to facilitate the study of gene expression variations and survival associations across tumors. Here, we report UALCAN, an easy to use, interactive web-portal to perform to in-depth analyses of TCGA gene expression data. UALCAN uses TCGA level 3 RNA-seq and clinical data from 31 cancer types. The portal's user-friendly features allow to perform: 1) analyze relative expression of a query gene(s) across tumor and normal samples, as well as in various tumor sub-groups based on individual cancer stages, tumor grade, race, body weight or other clinicopathologic features, 2) estimate the effect of gene expression level and clinicopathologic features on patient survival; and 3) identify the top over- and under-expressed (up and down-regulated) genes in individual cancer types. This resource serves as a platform for in silico validation of target genes and for identifying tumor sub-group specific candidate biomarkers. Thus, UALCAN web-portal could be extremely helpful in accelerating cancer research. UALCAN is publicly available at http://ualcan.path.uab.edu.
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              Cancer Statistics, 2017.

              Rebecca Siegel, Kimberly D Miller, Ahmedin Jemal (2017)
              Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data were collected by the National Center for Health Statistics. In 2017, 1,688,780 new cancer cases and 600,920 cancer deaths are projected to occur in the United States. For all sites combined, the cancer incidence rate is 20% higher in men than in women, while the cancer death rate is 40% higher. However, sex disparities vary by cancer type. For example, thyroid cancer incidence rates are 3-fold higher in women than in men (21 vs 7 per 100,000 population), despite equivalent death rates (0.5 per 100,000 population), largely reflecting sex differences in the "epidemic of diagnosis." Over the past decade of available data, the overall cancer incidence rate (2004-2013) was stable in women and declined by approximately 2% annually in men, while the cancer death rate (2005-2014) declined by about 1.5% annually in both men and women. From 1991 to 2014, the overall cancer death rate dropped 25%, translating to approximately 2,143,200 fewer cancer deaths than would have been expected if death rates had remained at their peak. Although the cancer death rate was 15% higher in blacks than in whites in 2014, increasing access to care as a result of the Patient Protection and Affordable Care Act may expedite the narrowing racial gap; from 2010 to 2015, the proportion of blacks who were uninsured halved, from 21% to 11%, as it did for Hispanics (31% to 16%). Gains in coverage for traditionally underserved Americans will facilitate the broader application of existing cancer control knowledge across every segment of the population. CA Cancer J Clin 2017;67:7-30. © 2017 American Cancer Society.
              Article 0 comments Cited 2052 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Xuai Lin:
                ORCID: https://orcid.org/0000-0001-8710-2330
                Journal
                Journal ID (nlm-ta): Comput Math Methods Med
                Journal ID (iso-abbrev): Comput Math Methods Med
                Journal ID (publisher-id): cmmm
                Title: Computational and Mathematical Methods in Medicine
                Publisher: Hindawi
                ISSN (Print): 1748-670X
                ISSN (Electronic): 1748-6718
                Publication date Collection: 2021
                Publication date (Electronic): 29 July 2021
                Volume: 2021
                Electronic Location Identifier: 9932088
                Affiliations
                1Department of Respiratory Medicine, Affiliated Hangzhou Chest Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
                2Department of Clinical Laboratory, Affiliated Hangzhou Chest Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
                3Department of Medical Microbiology and Parasitology, and Department of Infectious Diseases, Affiliated Children's Hospital, Zhejiang University School of Medicine, 866 Yuhangtang Road, Hangzhou 310058, China
                Author notes

                Academic Editor: Tao Huang

                Author information
                https://orcid.org/0000-0001-8563-1001
                https://orcid.org/0000-0003-4945-7434
                https://orcid.org/0000-0003-3762-432X
                https://orcid.org/0000-0001-8710-2330
                Article
                DOI: 10.1155/2021/9932088
                PMC ID: 8342145
                PubMed ID: 34367321
                SO-VID: a9bcf4a1-5374-4ecd-ae1a-e03d6b4dc8b1
                Copyright © Copyright © 2021 Aifeng Chen et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 24 March 2021
                Date revision received : 9 June 2021
                Date accepted : 26 June 2021
                Categories
                Subject: Research Article

                ScienceOpen disciplines: Applied mathematics
                Data availability:
                ScienceOpen disciplines: Applied mathematics

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